Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "113" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 86 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 84 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459903 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.440830 | 13.053035 | 3.867157 | 4.649888 | 7.098366 | 7.796026 | 3.412137 | 1.514582 | 0.0339 | 0.0307 | 0.0019 | nan | nan |
| 2459902 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.194611 | 14.541297 | 4.053825 | 4.706932 | 7.674927 | 8.151981 | 1.526460 | 0.637723 | 0.0367 | 0.0307 | 0.0035 | nan | nan |
| 2459901 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.409626 | 14.858381 | 3.580998 | 4.355187 | 6.985863 | 7.086949 | 1.431314 | 0.612256 | 0.0384 | 0.0310 | 0.0044 | nan | nan |
| 2459900 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.002075 | 15.058634 | 3.901452 | 4.757467 | 8.876606 | 7.599547 | 1.298411 | 0.561469 | 0.0356 | 0.0309 | 0.0030 | nan | nan |
| 2459898 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.596006 | 12.302647 | 3.484587 | 4.302161 | 8.139941 | 9.288873 | 2.160260 | 1.135137 | 0.0353 | 0.0309 | 0.0028 | nan | nan |
| 2459897 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.294170 | 12.048647 | 3.445267 | 4.468095 | 9.347939 | 9.933405 | 2.264890 | 1.019052 | 0.0342 | 0.0306 | 0.0023 | nan | nan |
| 2459896 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 11.586418 | 12.178839 | 3.737368 | 4.553044 | 9.859485 | 10.937124 | 1.542521 | 0.613400 | 0.0348 | 0.0306 | 0.0026 | nan | nan |
| 2459895 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.400247 | 14.938661 | 4.842829 | 5.472301 | 10.102827 | 10.971479 | 6.016907 | 5.491749 | 0.0351 | 0.0307 | 0.0030 | nan | nan |
| 2459894 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.420839 | 14.254803 | 3.019603 | 3.928055 | 9.702352 | 10.479753 | 1.945676 | 0.743114 | 0.0360 | 0.0308 | 0.0034 | nan | nan |
| 2459893 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.711649 | 14.682926 | 3.413784 | 4.313796 | 8.111307 | 9.237827 | 2.860695 | 1.469324 | 0.0357 | 0.0307 | 0.0030 | nan | nan |
| 2459892 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 13.815998 | 14.486200 | 4.062170 | 4.959188 | 6.515527 | 7.018168 | 2.158953 | 1.018180 | 0.0385 | 0.0311 | 0.0048 | nan | nan |
| 2459891 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.279050 | 13.324311 | 3.847893 | 4.906808 | 8.492090 | 10.352485 | 2.144808 | 1.160023 | 0.0351 | 0.0307 | 0.0027 | nan | nan |
| 2459890 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.921914 | 13.611979 | 4.799823 | 5.612753 | 8.080866 | 9.069373 | 0.906066 | -0.175549 | 0.0341 | 0.0308 | 0.0020 | nan | nan |
| 2459889 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 14.375223 | 15.368499 | 3.558296 | 4.620022 | 10.966863 | 12.881762 | 3.052664 | 1.297265 | 0.0364 | 0.0310 | 0.0033 | nan | nan |
| 2459888 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.042096 | 12.815486 | 4.643629 | 5.465487 | 9.848987 | 11.787880 | 2.392165 | 2.057200 | 0.0362 | 0.0308 | 0.0033 | nan | nan |
| 2459887 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.895705 | 13.696026 | 4.803014 | 5.371364 | 7.380940 | 10.296628 | 1.480593 | 0.754102 | 0.0365 | 0.0309 | 0.0033 | nan | nan |
| 2459886 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.993098 | 18.618152 | 4.210142 | 4.813103 | 14.553657 | 14.978108 | 3.927069 | 3.236074 | 0.0396 | 0.0312 | 0.0051 | nan | nan |
| 2459885 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 21.950170 | 22.441739 | 23.551904 | 26.014454 | 14.145887 | 19.401089 | 16.917254 | 11.542472 | 0.0355 | 0.0308 | 0.0026 | nan | nan |
| 2459884 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.317669 | 12.885468 | 4.450692 | 5.222909 | 7.956690 | 9.915553 | 1.526880 | 0.648268 | 0.0371 | 0.0312 | 0.0036 | nan | nan |
| 2459883 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.077965 | 17.917969 | 21.829704 | 23.866031 | 7.856229 | 10.840297 | 5.681985 | 3.276008 | 0.0388 | 0.0314 | 0.0047 | nan | nan |
| 2459882 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 28.426633 | 29.320604 | 23.203504 | 26.044085 | 11.125822 | 15.221884 | 2.306541 | 1.112326 | 0.0402 | 0.0320 | 0.0050 | nan | nan |
| 2459881 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 16.054580 | 16.725859 | 26.935571 | 29.872774 | 22.000013 | 30.505258 | 7.956442 | 13.655484 | 0.0379 | 0.0313 | 0.0040 | nan | nan |
| 2459880 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 19.536657 | 20.304672 | 22.869125 | 24.812129 | 6.747240 | 9.260832 | 2.772488 | 1.127504 | 0.0375 | 0.0316 | 0.0038 | nan | nan |
| 2459879 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 10.372082 | 10.896478 | 4.094860 | 4.787060 | 1.370322 | 2.015333 | 2.816725 | 1.551530 | 0.0374 | 0.0313 | 0.0035 | nan | nan |
| 2459878 | not_connected | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 17.598598 | 18.123279 | 27.750766 | 30.009272 | 11.515930 | 15.737673 | 7.264667 | 3.807254 | 0.0405 | 0.0321 | 0.0050 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.053035 | 13.053035 | 12.440830 | 4.649888 | 3.867157 | 7.796026 | 7.098366 | 1.514582 | 3.412137 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.541297 | 14.194611 | 14.541297 | 4.053825 | 4.706932 | 7.674927 | 8.151981 | 1.526460 | 0.637723 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.858381 | 14.409626 | 14.858381 | 3.580998 | 4.355187 | 6.985863 | 7.086949 | 1.431314 | 0.612256 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 15.058634 | 13.002075 | 15.058634 | 3.901452 | 4.757467 | 8.876606 | 7.599547 | 1.298411 | 0.561469 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.302647 | 12.302647 | 11.596006 | 4.302161 | 3.484587 | 9.288873 | 8.139941 | 1.135137 | 2.160260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.048647 | 12.048647 | 11.294170 | 4.468095 | 3.445267 | 9.933405 | 9.347939 | 1.019052 | 2.264890 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.178839 | 12.178839 | 11.586418 | 4.553044 | 3.737368 | 10.937124 | 9.859485 | 0.613400 | 1.542521 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.938661 | 14.400247 | 14.938661 | 4.842829 | 5.472301 | 10.102827 | 10.971479 | 6.016907 | 5.491749 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.254803 | 14.254803 | 13.420839 | 3.928055 | 3.019603 | 10.479753 | 9.702352 | 0.743114 | 1.945676 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.682926 | 13.711649 | 14.682926 | 3.413784 | 4.313796 | 8.111307 | 9.237827 | 2.860695 | 1.469324 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 14.486200 | 14.486200 | 13.815998 | 4.959188 | 4.062170 | 7.018168 | 6.515527 | 1.018180 | 2.158953 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.324311 | 12.279050 | 13.324311 | 3.847893 | 4.906808 | 8.492090 | 10.352485 | 2.144808 | 1.160023 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.611979 | 13.611979 | 12.921914 | 5.612753 | 4.799823 | 9.069373 | 8.080866 | -0.175549 | 0.906066 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 15.368499 | 14.375223 | 15.368499 | 3.558296 | 4.620022 | 10.966863 | 12.881762 | 3.052664 | 1.297265 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.815486 | 12.815486 | 12.042096 | 5.465487 | 4.643629 | 11.787880 | 9.848987 | 2.057200 | 2.392165 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 13.696026 | 13.696026 | 12.895705 | 5.371364 | 4.803014 | 10.296628 | 7.380940 | 0.754102 | 1.480593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 18.618152 | 17.993098 | 18.618152 | 4.210142 | 4.813103 | 14.553657 | 14.978108 | 3.927069 | 3.236074 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Power | 26.014454 | 22.441739 | 21.950170 | 26.014454 | 23.551904 | 19.401089 | 14.145887 | 11.542472 | 16.917254 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 12.885468 | 12.885468 | 12.317669 | 5.222909 | 4.450692 | 9.915553 | 7.956690 | 0.648268 | 1.526880 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Power | 23.866031 | 17.917969 | 17.077965 | 23.866031 | 21.829704 | 10.840297 | 7.856229 | 3.276008 | 5.681985 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 29.320604 | 29.320604 | 28.426633 | 26.044085 | 23.203504 | 15.221884 | 11.125822 | 1.112326 | 2.306541 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Temporal Variability | 30.505258 | 16.725859 | 16.054580 | 29.872774 | 26.935571 | 30.505258 | 22.000013 | 13.655484 | 7.956442 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Power | 24.812129 | 20.304672 | 19.536657 | 24.812129 | 22.869125 | 9.260832 | 6.747240 | 1.127504 | 2.772488 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Shape | 10.896478 | 10.896478 | 10.372082 | 4.787060 | 4.094860 | 2.015333 | 1.370322 | 1.551530 | 2.816725 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 113 | N11 | not_connected | nn Power | 30.009272 | 18.123279 | 17.598598 | 30.009272 | 27.750766 | 15.737673 | 11.515930 | 3.807254 | 7.264667 |